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在哪里可以找到有关Microsoft Dynamics CRM API的SOAP/XML查询信息？

xmlapisoapdynamics-crm
3
我正在为一位客户的项目工作，他希望将他们的网站（使用PHP构建）与他们托管在Microsoft Dynamics CRM 2011上的网站集成。
我在这里找到了一个很好的资源，可以连接到Dynamics服务器并从数据库中提取非常基本的信息（联系人/帐户）：
-> http://www.21logs.com/php-and-microsoft-dynamics-crm-source-code/ 今天我花了很多时间寻找其他类型的SOAP / REST查询的信息，以便使用它们来拉取更多信息或在数据库中添加/更新信息，但是没有太多收获。
如果有人知道有关使用XML示例查询以操作Dynamics CRM数据的其他资源，将不胜感激。
5个回答
3
那么我应该理解SOAP接口无法更新记录，只能检索它们吗？ - stevecomrie
这些资源看起来很有趣，但微软似乎没有在那些页面上提供任何上下文导航。是否有一个父文章链接到它们？我找不到任何链接返回到其他页面。 - Jason
3
虽然它并不是我最初寻找的信息库，但我发现了以下博客文章：
-> http://www.zenithies.org/articles/1/connect-to-microsoft-dynamics-crm-4-0-web-service-from-php-using-ifd-authentication.html 这引导我找到了这个：http://www.zenithies.org/articles/articles/6/microsoft-dynamics-crm-4-0-php-integration-offer.html Zenithies提供了一个PHP类以合理的价格出售（比我向客户收取研究和编写相同代码的费用低得多），它使您能够使用PHP连接到Dynamics CRM并执行联系人添加、编辑和搜索。
没有太复杂的东西，但这正是我需要的，可以将网站用户登录/自我管理地址信息与Microsoft Dynamics CRM连接起来。
我已购买该套餐并亲自尝试过，验证了其确实有效。我建议任何陷入需要连接PHP网站和Microsoft Dynamics CRM的情况中的人考虑只需进行相同的购买。
0

通过修改查询中的fetchxml，您可以获取大部分数据。您对哪种数据感兴趣？

我想在这种情况下，我正在寻找有关fetchxml的资源。 - stevecomrie
0

请查看我推送到GitHub上的稍作修改的Dynamics PHP SDK版本。

https://github.com/epinapala/MsDynamicsPHP

截至撰写本文时，Dynamics提供的SDK不能直接使用，需要进行更改。

希望这能有所帮助。

Rajesh

0
在Microsoft Dynamics中，如果您单击“高级视图”筛选器，则可以选择所需的数据并让系统自动生成fetchXML。我创建了一个R脚本，使用他们提供的Web API从Dynamics中提取数据。
整个脚本都在下面发布，希望你们中的一些人会发现它有用。很难过分强调这个Web API有多难以使用，找出所有这些内容是一个巨大的痛苦。
我在企业环境中工作，因此您将看到许多关于通过代理进行身份验证等代码。

enter image description here

#~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
#~~ Introduction
#~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
# Purpose: Download data from Microsoft Dynamics using a fetch XML request
# Created: 2/23/2021
# Modified: 10/1/2021
# Author: Ryan Bradley 
#
# Resources on this topic:
# https://github.com/r-lib/httr/blob/master/R/oauth-token.r
# https://github.com/r-lib/httr/blob/master/demo/oauth2-azure.r
# https://learn.microsoft.com/en-us/azure/active-directory/develop/quickstart-register-app
# https://blog.r-hub.io/2021/01/25/oauth-2.0/
# https://learn.microsoft.com/en-us/powerapps/developer/data-platform/authenticate-oauth
# https://learn.microsoft.com/en-us/dynamics365/customerengagement/on-premises/developer/webapi/discover-url-organization-web-api
# https://learn.microsoft.com/en-us/powerapps/developer/data-platform/webapi/retrieve-and-execute-predefined-queries#use-custom-fetchxml
# https://community.dynamics.com/365/f/dynamics-365-general-forum/378416/resource-not-found-for-the-segment-error-getting-custom-entity-from-common-data-service-web-api
# https://learn.microsoft.com/en-us/dynamics365/customerengagement/on-premises/developer/introduction-entities#:~:text=The%20entities%20are%20used%20to,Engagement%20(on%2Dpremises).&text=An%20entity%20has%20a%20set,%2C%20Address%20%2C%20and%20OwnerId%20attributes.
# https://datascienceplus.com/accessing-web-data-json-in-r-using-httr/
# https://learn.microsoft.com/en-us/powerapps/developer/data-platform/authenticate-oauth
# https://dev59.com/ZXA65IYBdhLWcg3w9DqF
# https://www.inogic.com/blog/2019/04/handling-special-characters-while-executing-fetch-xml-programmatically-in-web-api-rest-call/
# https://dev59.com/ZXA65IYBdhLWcg3w9DqF
# https://truenorthit.co.uk/2014/07/dynamics-crm-paging-cookies-some-gotchas/
#~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
#~~ First time set up and maintenance 
#~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
#...........................................................................................
#.. Encode your active directory user name and password with key ring
#...........................................................................................
# This is only relevant if you need to authenticate through a proxy.
#
# You will need to install the keyring package and set your active directory user name
# and password. You can run remove the comments from the 4 lines below and run them to
# install the package and set your AD user name and password.
#
# You will also need to repeat these steps when your active directory information changes.
#
# install.packages("keyring") 
# library(keyring)
# keyring::key_set("id")
# keyring::key_set("pw")
#
#...........................................................................................
#.. Get a token to authenticate with Microsoft Dynamics
#...........................................................................................
#
# 1. Log into the azure portal
#     https://portal.azure.com/#home
# 2. Register a new app
# 3. Generate a client secret on the "Certificates & secrets" page. Save it for later.
# 4. Create an application scope on the "Expose an API" page. 
# 5. Grant the app "user_impersonation" access to "Dynamics CRM"
# 6. Meet with the an Active Directory IT administrator, and have them click
#  "Grand Admin consent for Consumers Energy" on the "API Permissions" page of your app.
#
# If all those steps worked, you should now be able to authenticate using the code below.
#

#~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
#~~ Hard-coded variables
#~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

# Define global setting for importing strings as factors
options(stringsAsFactors = FALSE)
options(httr_oauth_cache=T)

#...........................................................................................
#.. Azure App Data
#...........................................................................................
# Found here:  https://portal.azure.com/#home

# Azure app ID
# Source: "Overview" tab of your application in the Azure portal
client_id <- "YOUR ID" 

# App name
# Source: "Overview" tab of your application in the Azure portal
app_name <- "MY_APP" # not important for authorization grant flow

# Secret ID
# Source: "Clients & Secrets" tab of your application in the Azure portal
client_secret <- "YOUR SECRET"

# Application ID URI.
# Source: "Expose an API" tab of your application in the Azure portal
application_id_uri = "YOUR URI "

#~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
#~~ Load or install packages
#~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
# Load or install librarian
if(require(librarian) == FALSE){
  install.packages("librarian")
  if(require(librarian)== FALSE){stop("Unable to install and load librarian")}
}

# Load multiple packages using the librarian package
librarian::shelf(tidyverse, readxl, RODBC, lubridate, httr, XML, jsonlite, rlist, httpuv, quiet = TRUE)

#~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
#~~  Set up global httr proxy configuration
#~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
# This is only needed if you're connecting through a proxy 

# set_config(use_proxy(url="yourproxy.com",port= 1234
#                      ,username=keyring::key_get("id")
#                      ,password=keyring::key_get("pw")
#                     )
#             )

#~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
#~~ Authenticate with Dynamics
#~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

# Use the default Azure endpoints default ones
azure_endpoint <- oauth_endpoints("azure")

# Create the app instance.
myapp <- oauth_app(
  appname = app_name,
  key = client_id,
  secret = client_secret
)

# Step through the authorization chain:
#    1. You will be redirected to you authorization endpoint via web browser.
#    2. Once you responded to the request, the endpoint will redirect you to
#       the local address specified by httr.
#    3. httr will acquire the authorization code (or error) from the data
#       posted to the redirect URI.
#    4. If a code was acquired, httr will contact your authorized token access
#       endpoint to obtain the token.
mytoken <- oauth2.0_token(azure_endpoint,
                          myapp,
                          scope = application_id_uri,
                          cache = str_cache
)

if (("error" %in% names(mytoken$credentials)) && (nchar(mytoken$credentials$error) > 0)) {
  errorMsg <- paste("Error while acquiring token.",
                    paste("Error message:", mytoken$credentials$error),
                    paste("Error description:", mytoken$credentials$error_description),
                    paste("Error code:", mytoken$credentials$error_codes),
                    sep = "\n"
  )
  stop(errorMsg)
}

#~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
#~~ Begin making requests
#~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

#...........................................................................................
#.. Dynamics API data
#...........................................................................................

# The URL of the Dynamics instace you'd like to connect to
base_url <- "https://YOURPLATFORM.dynamics.com/api/data/v9.2/"

# This part of the URL is added on to the base URL to use the web API 
# to send on a fetchxml request
url_add_on <- "?fetchXml="

# XML retrieved by downloading fetch XML from Microsoft Dyanmics using the "Advanced Find" view. 
# You may need to edit the auto-generated XML, it's not always great. Consider making the alias
# something legible, like I did below.
#
# I copied and pasted the XML into notepad and used find and replace to escape all the quotes.
# " -> \"
#
# Make sure your "order attribute" XML code is unique to every row in the data set. 
# If it's not, if may cause issues when pulling data with more than 5,000 rows.
#
xml <- "<fetch version=\"1.0\" output-format=\"xml-platform\" mapping=\"logical\" distinct=\"false\">
  <entity name=\"amendments\">
    <attribute name=\"seasonyear\" />
    <attribute name=\"enrollmenttype\" />
    <attribute name=\"effectivestartdate\" />
    <attribute name=\"effectiveenddate\" />
    <attribute name=\"contractstartdate\" />
    <attribute name=\"contractenddate\" />
    <attribute name=\"program\" />
    <attribute name=\"programduration\" />
    <attribute name=\"programtype\" />
    <attribute name=\"kwnominations\" />
    <attribute name=\"renewalkw\" />
    <attribute name=\"newkw\" />
    <attribute name=\"account\" />
    <attribute name=\"ownerid\" />
    <attribute name=\"amendmentsid\" />
    <attribute name=\"statuscode\" />
    <attribute name=\"statecode\" />
    <attribute name=\"amendment\" />
    <attribute name=\"name\" />
    <order attribute=\"amendmentsid\" descending=\"false\" />
    <link-entity name=\"salesorder\" from=\"salesorderid\" to=\"order\" visible=\"false\" link-type=\"outer\" alias=\"sales\">
      <attribute name=\"datecontractapproved\" />
    </link-entity>
    <link-entity name=\"account\" from=\"accountid\" to=\"account\" visible=\"false\" link-type=\"outer\" alias=\"account\">
      <attribute name=\"accountmanager\" />
      <attribute name=\"accountnumber\" />
      <attribute name=\"statecode\" />
    </link-entity>
  </entity>
</fetch>"

# Encode the XML into a URL
url_xml <- URLencode(xml)

# Set the dynamics entity/table you wish to use. 
# Entities = Dynamics tables, and attributes = Dynamics columns
# NOTE: MUST BE PLURAL. If your entity is "contact" then put "contacts" and if
# your entity already ends in "s" try adding "es." Example -> amendments -> amendmentses
#
# You should be able to find the entity name in the first or 2nd line of an auto-generated
# XML. Example:   <entity name=\"amendments\">
dynamics_entity <- "amendmentses"

# Create a Web API query URL
url_fetch <- paste0(base_url, dynamics_entity, url_add_on, url_xml)
# url_fetch

# Send GET request. 
resp <- GET(url = url_fetch
            , config(token = mytoken)
            , add_headers(Prefer = "odata.include-annotations=\"*\"") # This header is required to get legible text returned along with a paging cookies (if applicable)
)

#...........................................................................................
#.. Check for a valid API response
#...........................................................................................
if(http_error(resp) == TRUE){
  print("Authentication error, unable to proceed.")
} else {
  
  # Convert the hexadecimal content response to a string
  resp_json <- rawToChar(resp[["content"]])
  
  # Decode the JSON response
  resp_list <- fromJSON(resp_json)
  
  # Extract the data frame values into a stand-alone data frame
  df_data_raw <- resp_list[["value"]]
  
  # Extract paging cookie data (This is only passed if there is more than 1 page of results.
  # by default an API query is limited to 5000 rows, so any extra rows are on additional 
  # pages that need to be queried.)
  paging_cookie_resp <- resp_list[["@Microsoft.Dynamics.CRM.fetchxmlpagingcookie"]]
  
  # Check for a paging cookie
  if(length(paging_cookie_resp) == 0){
    print("No paging cookie returned, only one page of results.")
  } else {
    
    print("Retrieving multiple pages of results.")
    
    # Set the starting page number
    page_number <- 1
    
    # Create a variable to determine when we've found the last page of data
    last_page_found <- FALSE
    
    while(last_page_found == FALSE){
      
      #...........................................................................................
      #.. Retrieve multiple pages of results (only applies to data sets with > 5,000 rows)
      #...........................................................................................
      # Split the paging cookie data into a list
      lst_paging_cookie_resp <- str_split(paging_cookie_resp,"\"")
      
      # Retrieve the double-URL-encoded paging cookie
      encoded_paging_cookie <- lst_paging_cookie_resp[[1]][4]
      
      # The paging cookie is DOUBLE url-encoded, so you first need to decode it TWICE. (What a pain this was to figure out)
      decoded_paging_cookie <- URLdecode(URLdecode(encoded_paging_cookie))
      
      # Split the de-coded paging cookie data into a list (so we can extract the page number)
      lst_decoded_paging_data <- str_split(decoded_paging_cookie,"\"")
      
      # If the paging cookie comes in double-quotes, remove the the quotes at the 
      # beginning and end of the string
      decoded_paging_cookie <- str_remove(decoded_paging_cookie,"^\"")
      decoded_paging_cookie <- str_remove(decoded_paging_cookie,"\"$")
      
      # Replace any special characters with their HTML equivalents 
      decoded_paging_cookie <- str_replace_all(decoded_paging_cookie,"&","&amp;")
      decoded_paging_cookie <- str_replace_all(decoded_paging_cookie,"<","&lt;")
      decoded_paging_cookie <- str_replace_all(decoded_paging_cookie,">","&gt;")
      decoded_paging_cookie <- str_replace_all(decoded_paging_cookie,"\"","&quot;")
      
      # URI encode to the paging cookie (This must be done so the API can receive it)
      URI_encoded_paging_cookie <- encodeURIComponent(decoded_paging_cookie)
      
      # Increment the page number by 1
      page_number = page_number + 1
      
      # Create a URL-encoded fetch-XML header that we can add into the existing the URL-encoded XML that 
      # We originally sent to the API
      xml_header <- "paging-cookie=\"PutPagingCookieHere\" page=\"PutPageNumberHere\" distinct="
      url_encoded_xml_header <- URLencode(xml_header)
      
      # Splice in the URI-encoded paging cookie and page number
      url_encoded_xml_header <- url_encoded_xml_header %>%
        str_replace("PutPagingCookieHere",URI_encoded_paging_cookie) %>%
        str_replace("PutPageNumberHere",as.character(page_number))
      
      # We now have the paging cookie and page number in the appropriate URL and URI encoded formats. 
      # We can now splice this extra information into the XML header of our original API request. 
      new_url_xml <- str_replace(url_xml,"distinct=",url_encoded_xml_header)
      
      # Create a new Web API query URL with the updated XML data
      url_fetch <- paste0(base_url, dynamics_entity, url_add_on, new_url_xml) 
      # url_fetch
      
      # Retrieve the next page of data
      resp <- GET(url = url_fetch
                  , config(token = mytoken)
                  , add_headers(Prefer = "odata.include-annotations=\"*\"") # This header is required to get legible text returned
      )
      
      # Check for an error returned in the response
      if(http_error(resp) == TRUE){
        
        print("Error while retrieving 2nd page of results, unable to proceed.")
        last_page_found <- TRUE
        
      } else {
        
        # Convert the hexadecimal content response to a string
        resp_json <- rawToChar(resp[["content"]])
        
        # Decode the JSON response
        resp_list <- fromJSON(resp_json)
        
        # Extract the data frame values into a stand-alone data frame
        df_data_raw_next_page <- resp_list[["value"]]
        
        # The API only returns columns that hold data. To make sure our columns match, 
        # we need to add any columns missing from either data frame to the other data frame
        # so we can join them.
        
        # Add any columns missing from the original data frame to the new one
        prev_page_names <- names(df_data_raw)  # Vector of columns you want in this data.frame
        missing <- setdiff(prev_page_names, names(df_data_raw_next_page))  # Find names of missing columns
        df_data_raw_next_page[missing] <- NA                    # Add them, filled with NA results
        
        # Add any columns missing from the new data frame to the original one
        next_page_names <- names(df_data_raw_next_page)  # Vector of columns you want in this data.frame
        missing <- setdiff(next_page_names, names(df_data_raw))  # Find names of missing columns
        df_data_raw[missing] <- NA                    # Add them, filled with NA results
        
        # Append these rows onto the original data frame and
        # filter out any extra rows from the join
        df_data_raw <- df_data_raw %>%
          rbind(df_data_raw_next_page, use.names=TRUE) %>%
          filter(`@odata.etag` != "TRUE") %>%
          distinct()
        
        # Extract paging cookie data (This is only passed if there is more than 1 page of results.
        # by default an API query is limited to 5000 rows, so any extra rows are on additional 
        # pages that need to be queried.)
        paging_cookie_resp <- resp_list[["@Microsoft.Dynamics.CRM.fetchxmlpagingcookie"]]
        
        # Note if we're on the last page of results so we exit the loop
        if(nrow(df_data_raw_next_page) < 5000){
          last_page_found <- TRUE 
        } else {
          print(paste0("Page ",page_number," retrieved, retrieving page ", page_number + 1))
        }
      }
    }
  }
  
  #...........................................................................................
  #.. Clean the returned column names
  #...........................................................................................
  
  # Keep formatted columns only, removing the non-formatted versions from the data frame.
  # The API gives 2 versions of each formatted column, a formatted version and a non-formatted version with the GUID.
  # We only want the formatted version, since that's readable to the human eye. We don't want a GUID.
  
  # Set the starting index to 1
  i <- 1
  
  # Loop over the columns in the data frame
  while(i <= length(names(df_data_raw))){
    
    # Extract the current column name
    str_col_name <-names(df_data_raw)[i]
    # print(paste0("Cleaning column ",i," - ",str_col_name))
    
    # Proceed if we have a column returned
    if(is.na(str_col_name) == FALSE){
      
      # Check for unwanted meta data columns we can remove
      condition_1 <- grepl("@Microsoft.Dynamics.CRM",str_col_name, ignore.case = TRUE)
      condition_2 <- grepl("Display.V1.AttributeName",str_col_name, ignore.case = TRUE)
      condition_3 <- grepl("@odata_etag",str_col_name, ignore.case = TRUE)
      condition_4 <- grepl("@odata.etag",str_col_name, ignore.case = TRUE)
      
      # Check to see if an unwanted column has been found
      if(condition_1 | condition_2 | condition_3 | condition_4){
        
        # Remove the column
        df_data_raw <- df_data_raw %>%
          select(-all_of(str_col_name))
        
        # Reset the index since a column was removed
        i <- 0
        
      } else {
        
        # Check to see if it's formatted column
        if(grepl("@OData.Community.Display.V1.FormattedValue",str_col_name, ignore.case = TRUE)){
          
          # Extract the base column name by removing the huge suffix "@OData.Community.Display.V1.FormattedValue"
          str_base_col <- str_replace(str_col_name,"@OData.Community.Display.V1.FormattedValue","")
          
          # Remove the base column if it exists (leaving us with only the formatted version of 
          # the column, not the original version of it.)
          df_data_raw <- df_data_raw %>%
            select(-all_of(str_base_col))
          
          # Reset the index since a column was removed
          i <- 0
          
        } else {
          str_base_col <- str_col_name
        }
        
        # Remove any prefixes or suffixes from the column name
        str_new_col <- str_replace(str_base_col,"_","") # Remove "_" prefix
        str_new_col <- str_replace(str_new_col,"\\.","_") # Replace any periods with an underscore
        str_new_col <- str_replace(str_new_col,"_value$","") # Remove "_value" suffix
        
        # Re-name the old column name to the new one
        df_data_raw <- df_data_raw %>%
          rename(!!str_new_col := all_of(str_col_name))
        
        # If this is a character column that has the word "date" in it attempt to convert it to the 
        # a date-type column. 
        if(grepl("date",str_new_col,ignore.case = TRUE) & typeof(df_data_raw[,i]) == "character"){
          
          print(paste0("Attempting to convert ",str_new_col," to a date format."))
          # Attempt to assign the proper data type
          df_data_raw <- df_data_raw %>%
            mutate(attempt_mdy = mdy(!!as.symbol(str_new_col))) 
          
          # If we had at least 1 successful conversion, convert the
          # column to the date format
          if(sum(is.na(df_data_raw$attempt_mdy) == FALSE) > 0){
            df_data_raw <- df_data_raw %>%
              mutate(!!as.symbol(str_new_col) := attempt_mdy)
          }
          
          # Drop the attempt_mdy column
          df_data_raw <- df_data_raw %>%
            select(-attempt_mdy)
        }
      }
    }
    # Move up the index to the next column
    i <- i + 1
  }
  
  # Select desired columns
  df_data <- df_data_raw %>%
    select(contract_account_number = account_accountnumber       
           , program_duration = programduration
           , contract_start_date = contractstartdate
           , contract_end_date = contractenddate
           , season_year = seasonyear
           , state_code = statecode
           , status_code = statuscode
           , account
           , account_state_code = account_statecode
           , program
           , name
           , kw_new = newkw
           , kw_renewal = renewalkw
           , kw_nomination = kwnominations
           , account_manager = account_accountmanager
           , contract_approval_date = sales_datecontractapproved
           , enrollment_type = enrollmenttype
           , effective_start_date = effectivestartdate
           , effective_end_date = effectiveenddate
           , owner = ownerid
           , program_type = programtype
           , amendment
           , amendment_id = amendmentsid
    )
  
  # Set numeric data types
  df_data <- df_data %>%
    # Remove commas
    mutate(season_year = gsub(",","",season_year)
           , kw_new = gsub(",","",kw_new)
           , kw_renewal = gsub(",","",kw_renewal)
           , kw_nomination = gsub(",","",kw_nomination)) %>%
    # Convert to numeric values
    mutate(season_year = as.numeric(season_year)
           , kw_new = as.numeric(kw_new)
           , kw_renewal = as.numeric(kw_renewal)
           , kw_nomination = as.numeric(kw_nomination))
  
  # Add a load_date_time column
  df_data$load_date_time <- Sys.time()
  
  
}
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